Depth-Entropy Guided Sampling Improves LLM Reasoning Without Training

Jul 14, 2026

A new method called Depth-Entropy Guided Sampling (DEGS) leverages layer-wise entropy collapse as a quality signal to enhance large language model (LLM) reasoning at test time, without requiring any additional training. DEGS integrates sequence likelihood with collapse depth in a Markov Chain Monte Carlo (MCMC) framework, achieving state-of-the-art accuracy among training-free methods on several reasoning benchmarks. The approach demonstrates particular strength out-of-domain and on challenging tasks, sometimes surpassing reinforcement learning (RL)-trained models, all with minimal computational overhead.

Why it matters: DEGS offers a practical, training-free alternative to RL for boosting LLM reasoning, potentially lowering the cost and complexity of deploying advanced reasoning systems.

Full story at: arXiv Machine Learning ↗